Will machine learning surpass human learning?

Advances in machine learning are powering the current boom in AI, transforming industry and many everyday tasks. But what does the future of machine learning hold?

Today, artificial intelligence is everywhere. It’s in our phones, powering voice assistants such as Siri and Alexa. It’s in our GPS systems, giving us the most efficient route to get home. And it’s in our social media networks, giving us personalised news feeds and targeted advertising.

All these applications of artificial intelligence rely on machine learning (ML), where algorithms are taught to act with similar cognition to the human brain. This self-education has already transformed many everyday tasks and continues to transform industries. Its benefits are clear: instead of relying on humans to process information and make decisions (which can be time consuming and costly), machines can make the same judgements almost seamlessly.

But what happens when machine learning goes down the other path – the one that dystopian sci-fi thrillers love to explore? Is an Ex Machina-style future possible, where machine learning surpasses human learning? And even if not, what are the other consequences we need to consider as we build this technology?

AI and the different types of machine learning

To understand machine learning, it’s important to define it. In 1943, scientists discovered that ‘artificial neurons’ could carry out logical functions such as human-style learning. However, the term ‘artificial intelligence’ wasn’t coined until 1956, when John McCarthy used the term to sum up a range of advanced research topics during a science conference held at Dartmouth University.

Today, AI refers to the general capability of a machine being able to imitate human behaviour. This includes tasks such as understanding language, perceiving objects and surroundings, as well as continued learning.

There are two main types of AI:

Credit: Ioannis Oikonomou

“One of the most important challenges we face today is ensuring we design positive values into systems that use machine learning.”

—Nicholas Davis

Narrow AI

The AI can exhibit some aspects of human intelligence, but is lacking in others. For the traits that the AI can demonstrate, it has the ability to do that task extremely well. For example, consider DeepMind’s AlphaGo AI. It can defeat the world champion of the game Go, but it can’t do much more than that.

General AI

The AI has all the characteristics of human intelligence. This include tasks such as reasoning, planning, abstract thinking and learning from experience. This is a huge step-up from narrow AI and is yet to be fully achieved.

“Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years”

Where machine learning fits in

Machine learning supports the goals of AI. It’s not the only way to achieve AI – but it is the most successful so far.

This means it’s possible to build AI without machine learning. Just think of your own intelligence. Sure, pattern recognition is partly how you’ve learned to speak, calculate and read – but this learning must be paired with experimentation, decision-making and emotion for it to be complete. As such, AI – especially general AI – requires more than just looking at algorithms, decision-trees and data.

In that sense, machine learning can be seen as a clever processing technique. The term ‘machine learning’ was first coined in 1959 by Arthur Samuel who defined it as “the ability to learn without being explicitly programmed”. Instead of being fed instructions, the machine’s algorithm is ‘trained’ to self-adjust and improve to compute data better. Machines find a solution based on the data they have.

It allows engineers to make the most of data without having to explicitly program machines to follow set paths.

There are several variations of machine learning. These include:

Representation learning

This is where systems are designed to automatically discover the representations needed for data classification or detection. Different forms of representation learning include:

This is mainly pattern recognition. It relies on data being labelled correctly. Labels are usually tagged by humans to guarantee data quality. For example, a computer is trying to learn the difference between an apple and orange. Each image is tagged ‘apple’ and ‘orange’ accordingly. The machine learns the difference, meaning the algorithm can classify unseen images as ‘apple’ or ‘orange’.

In this case, there are no labels. The algorithm looks for structure within the data. Once it finds examples that are similar to each other, it groups them into clusters it can then learn from. For example, in 2012, Google made a huge breakthrough: it managed to train its computers to recognise cats in YouTube videos.

With reinforcement learning, the algorithm figures out what is the best solution through a number of trial and error experiences. It builds up its ‘knowledge’ through a series of training examples (e.g. this action was positive, this action was negative). A computer that improves its chess skills with each game it plays is an example of reinforcement learning.

Deep learning

Deep learning refers to when there are a number of hidden layers in an artificial neural network (ANN). These layers are designed to mimic the biological way the human brain processes information – be it turning sound into speech recognition, or images into information. Within the ANN, there are layers and connections to other ‘neurons’. Each layer is dedicated to learning a particular aspect of a task, and multiple layers are used to complete the whole task.

Companies using machine learning

Today, most big tech companies are investing in at least some form of machine learning.

Facebook

Facebook is using machine learning to predict user actions. This includes pre-empting things such as what a user will ‘Like’ and what posts they’ll click on. Machine learning is also being used to order the News Feed and make recommendations.

Google

Google is often regarded as the most advanced company when it comes to AI and machine learning. As well as its continual acquisitions of AI startups, it offers developers a range of cloud-based tools to encourage further development in the field. One such example is its Google Cloud AI machine learning tool.

Microsoft

In 2017, Microsoft acquired the deep learning startup Maluuba, which it described as “one of the world’s most impressive deep learning research labs for natural language understanding”. It hopes this acquisition will advance its progress in creating fully literate machines.

He gathered roughly 1,170 photos from each of the 41 shops of the Tokyo-based chain Ramen Jiro. He then fed the images to the AI software. AutoML took about 24 hours to finish training the data, after which the model was able to predict which shop a bowl of ramen came from with 95% accuracy.

Machine learning vs human learning

On an extremely basic level, it could simply be said that human knowledge resides in the brain, whereas machine knowledge exists on servers. But the differences and similarities between the two go much further than that.

While artificial neural networks may mimic human brain functions, they still haven’t achieved the same level of human intelligence. This is because artificial neurons cannot self-organise and adapt in the same way human neurons can. As well as that, machine learning cannot be programmed to include intrinsic human learning characteristics.

One such characteristic is motivation. Humans learn because we enjoy doing it and find it personally rewarding. Machines, on the other hand, can only be motivated to do things for external rewards or to avoid negative consequences.

Credit: Roman Mager

Artificial neural networks may be able to mimic human brain functions, but they still haven’t achieved the same level of intelligence as humans

Combining human and machine learning

But as it stands, machines can’t apply knowledge to think in more abstract ways.

However, as our propensity to rely on automated tasks grows (after all, many answers to our questions are a mere Google away), human learning may evolve differently. We can expect less of a focus on information retention in tomorrow’s classrooms, and more of a focus on problem-solving and creativity.

The researchers used robots and infants to examine the role body position had in the brain’s ability to ‘map’ names to objects. The study discovered that consistency of the body’s posture and spatial relationship to an object, as the object’s name was shown and said aloud, were key to successfully connecting the name to the object.

“This study shows that the body plays a role in early object name learning, and how toddlers use the body’s position in space to connect ideas. The creation of a robot model for infant learning has far-reaching implications for how the brains of young people work.”

Should we all be worried?

The current hype around AI means there has been a lot of fear around the potential negative consequences of machine learning. But this attitude neglects to consider the benefits of joint human and machine learning.

Credit: Stefano Pollio

“One of the most important challenges we face today is ensuring we design positive values into systems that use machine learning.”

—Nicholas Davis

Singularity: a potential tomorrow

Much fear around machine learning comes from singularity, a hypothetical point where AI and robots surpass human intelligence. The term comes from the gravitational singularity that occurs at the centre of a black hole, where gravitational fields are infinitely strong and the laws of physics collapse. In 1993, Vernor Vinge wrote an essay in which he applied the term to a moment in the future when technology’s intelligence exceeds our own, and life as we know it will be forever changed. It’s a theme that has formed the basis of many science-fiction stories (and usually for the worse).

Multiplicity: where we are today

This is when people and machines work together to solve problems. It doesn’t exist in the realm of science fiction – rather, it already exists in many smart systems we have today. Humans are essential to multiplicity. Diverse groups of people interact with diverse groups of machines to translate languages, make recommendations on books, and suggest tags for images and videos. This is the approach many researchers are taking with machine learning.

In March 2018, the World Economic Forum released a paper on how developers could prevent discrimination of humans in machine learning. It looked at how, without proper care, machine learning has the potential to further marginalise and discriminate against certain groups, based on the biased data they base their training on.

The key recommendation is for developers and businesses to prioritise non-discrimination by adopting four key principles:

Active inclusion

Fairness

Right to understand

Access to redress

“One of the most important challenges we face today is ensuring we design positive values into systems that use machine learning. This means deeply understanding how and where we bias systems and creating innovative ways to protect people from being discriminated against.”

The future of machine learning

Humans and machines need to evolve with each other, and not in silos apart. With smarter machines, our human abilities become augmented. Just as the printing press democratised knowledge and information in the 15th century, computers with AI are doing the same now.

But as machine learning evolves, we’ll have to carefully consider a number of complex and far-reaching questions surrounding its applications. How can we make sure machine learning doesn’t promote systemic bias that comes from existing data sets? And how can we make sure humans remain purposeful in a world without work?

Because while we’re still coming to grips with narrow AI, general AI may eventually come to fruition. And once that happens, the next hurdle will be superintelligent AI. Fingers crossed an intelligent machine can help us tackle that momentous step.